I just came across a very interesting Google Video from Google. Luis von Ahn, a Carnegie Mellon professor, discusses the Captcha concept used to stop automatic systems from filling in forms and how currently Spam companies are trying to get around it.
Captcha is used to describe a system that allows a computer to automatically generate random numbers and or letters and then obfuscate them in order to render reading them by a computer highly unlikely or impossible.
This was interesting for me since I had worked on a similar concept for stopping people from requesting lost passwords on Significant-Media.
The video below then goes on to explain how human computation i.e. people solving problems rather than computers can be useful. With the Captcha system for example, computers are not capable of providing correct answers.
Then the ESP Game is explained and the process for it is pretty amazing from a thought process point of view. Anyone who has to come up with new ideas in their company should find this part truly fascinating :
The part I found really impressive, in view of a successive set of procedures, was when von Ahn explains the different combinations of possible player modes, as well as anti-cheating mechanisms...
The guy seems like a great teacher as well, if the seminar is anything to go by !
Placed in context this image tagging system is what early users of flickr were used to when they add tags to their own photos and is now pretty common procedure. Flickr was bought by Yahoo and Google well may have found a novel way of catching up here, if you read between the lines...
Update : interesting article over at the washingtonpost.com concerning a way to 'turn the tables' on the phishing techniques using images from the banks
I question the reliability and accuracy of Generative AI (GenAI) in enterprise scenarios, particularly when faced with adversarial questions, highlighting that current Large Language Models (LLMs) may be data-rich but lack in reasoning and causality. I would call for a more balanced approach to AI adoption in cases of assisting users, requiring supervision, and the need for better LLM models that can be trusted, learn, and reason.
I discuss my experience with chatbots, contrasting older rules-based systems with newer GenAI (General Artificial Intelligence) chatbots. We cannot dismiss the creative capabilities of GenAI-based chatbots, but these systems lack reliability, especially in customer-facing applications, and improvements in the way AI is structured could lead to a "software renaissance," potentially reducing society's technical debt.
The article discusses the contrasting debate on how AI safety is and should be managed, its impact on technical debt, and its societal implications.
It notes the Center for AI Safety's call for a worldwide focus on the risks of AI, and Meredith Whittaker's criticism that such warnings preserve the status quo, strengthening tech giants' dominance. The piece also highlights AI's potential to decrease societal and technical debt by making software production cheaper, simpler, and resulting in far more innovation. It provides examples of cost-effective open-source models that perform well and emphasizes the rapid pace of AI innovation. Last, the article emphasises the need for adaptive legislation to match the pace of AI innovation, empowering suitable government entities for oversight, defining appropriate scopes for legislation and regulation, addressing ethical issues and biases in AI, and promoting public engagement in AI regulatory decisions.
Japan has made its ruling on the situation between Content creators and Businesses. Japanese companies that use AI have the freedom to use content for training purposes without the burden of copyright laws. This news about the copyright laws in Japan reported over at Technomancers is seen as Businesses: 1 / Content Creators: 0 The […]